Many modern Field Programmable Logic Arrays (FPGAs) use lookup table (LUT) logic blocks which can be programmed to realize any function of a fixed number of inputs. It is possible to employ logic blocks that realize only a subset of all functions, while the rest can be obtained by permuting and negating the inputs. Such blocks, known as Universal Logic Modules (ULMs), have already been consider...

Complex animal behaviors are built from dynamical relationships between sensory inputs, neuronal activity, and motor outputs in patterns with strategic value. Connecting these patterns illuminates how nervous systems compute behavior. Here, we study Drosophila larva navigation up temperature gradients toward preferred temperatures (positive thermotaxis). By tracking the movements of animals res...

A novel method of generating a pseudorandom binary sequence (PRBS) test signal generator is presented. A traditional PRBS generator uses a linear feedback shift register (LFSR) which generates a single maximum length sequence pattern having a fixed period. In this paper, three shift registers with feedback connections are employed in which the preset inputs of the shift registers are changed on...

We propose an Encoder-Classifier framework to model the Mandarin tones using recurrent neural networks (RNN). In this framework, extracted frames of features for tone classification are fed in to the RNN and casted into a fixed dimensional vector (tone embedding) and then classified into tone types using a softmax layer along with other auxiliary inputs. We investigate various configurations th...

A key component of any reinforcement learning algorithm is the underlying representation used by the agent. While reinforcement learning (RL) agents have typically relied on hand-coded state representations, there has been a growing interest in learning this representation. While inputs to an agent are typically fixed (i.e., state variables represent sensors on a robot), it is desirable to auto...

Correspondence: [email protected] Computing and Mathematics, University of Stirling, FK9 4LA Stirling, UK Abstract Background: Mixed Order Hyper Networks (MOHNs) are a type of neural network in which the interactions between inputs are modelled explicitly by weights that can connect any number of neurons. Such networks have a human readability that networks with hidden units lack. They can be u...

We investigate elastic block ciphers, a method for constructing variable length block ciphers, from a theoretical perspective. We view the underlying structure of an elastic block cipher as a network, which we refer to as an elastic network, and analyze the network in a manner similar to the analysis performed by Luby and Rackoff on Feistel networks. We prove that a three round elastic network ...

We consider the k-Center problem and some generalizations. For k-Center a set of k center vertices needs to be found in a graph G with edge lengths, such that the distance from any vertex of G to its nearest center is minimized. This problem naturally occurs in transportation networks, and therefore we model the inputs as graphs with bounded highway dimension, as proposed by Abraham et al. [SOD...

This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the nu...

Nonrobustness refers to qualitative or catastrophic failures in geometric algorithms arising from numerical errors. Section 45.1 provides background on these problems. Although nonrobustness is already an issue in “purely numerical” computation, the problem is compounded in “geometric computation.” In Section 45.2 we characterize such computations. Researchers trying to create robust geometric ...